
"""simple docstring"""



import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


a       :						Any       		=		logging.getLogger(__name__)

a       :						Tuple       		=		tf.data.AUTOTUNE


def 			_UpperCamelCase						(    )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = argparse.ArgumentParser(description="""Train a masked language model on TPU."""   )
    parser.add_argument(
        """--pretrained_model_config"""       ,   type=_A       ,   default="""roberta-base"""       ,   help="""The model config to use. Note that we don't copy the model's weights, only the config!"""       ,   )
    parser.add_argument(
        """--tokenizer"""       ,   type=_A       ,   default="""unigram-tokenizer-wikitext"""       ,   help="""The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."""       ,   )

    parser.add_argument(
        """--per_replica_batch_size"""       ,   type=_A       ,   default=8       ,   help="""Batch size per TPU core."""       ,   )

    parser.add_argument(
        """--no_tpu"""       ,   action="""store_true"""       ,   help="""If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."""       ,   )

    parser.add_argument(
        """--tpu_name"""       ,   type=_A       ,   help="""Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."""       ,   default="""local"""       ,   )

    parser.add_argument(
        """--tpu_zone"""       ,   type=_A       ,   help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."""       ,   )

    parser.add_argument(
        """--gcp_project"""       ,   type=_A       ,   help="""Google cloud project name. Only used for non-Colab TPU nodes."""   )

    parser.add_argument(
        """--bfloat16"""       ,   action="""store_true"""       ,   help="""Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."""       ,   )

    parser.add_argument(
        """--train_dataset"""       ,   type=_A       ,   help="""Path to training dataset to load. If the path begins with `gs://`"""
        """ then the dataset will be loaded from a Google Cloud Storage bucket."""       ,   )

    parser.add_argument(
        """--shuffle_buffer_size"""       ,   type=_A       ,   default=2**1_8       ,   help="""Size of the shuffle buffer (in samples)"""       ,   )

    parser.add_argument(
        """--eval_dataset"""       ,   type=_A       ,   help="""Path to evaluation dataset to load. If the path begins with `gs://`"""
        """ then the dataset will be loaded from a Google Cloud Storage bucket."""       ,   )

    parser.add_argument(
        """--num_epochs"""       ,   type=_A       ,   default=1       ,   help="""Number of epochs to train for."""       ,   )

    parser.add_argument(
        """--learning_rate"""       ,   type=_A       ,   default=1e-4       ,   help="""Learning rate to use for training."""       ,   )

    parser.add_argument(
        """--weight_decay_rate"""       ,   type=_A       ,   default=1e-3       ,   help="""Weight decay rate to use for training."""       ,   )

    parser.add_argument(
        """--max_length"""       ,   type=_A       ,   default=5_1_2       ,   help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"""       ,   )

    parser.add_argument(
        """--mlm_probability"""       ,   type=_A       ,   default=0.15       ,   help="""Fraction of tokens to mask during training."""       ,   )

    parser.add_argument("""--output_dir"""       ,   type=_A       ,   required=_A       ,   help="""Path to save model checkpoints to."""   )
    parser.add_argument("""--hub_model_id"""       ,   type=_A       ,   help="""Model ID to upload to on the Hugging Face Hub."""   )

    _UpperCAmelCase        = parser.parse_args()
    return args


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    try:
        if args.tpu_name:
            _UpperCAmelCase        = tf.distribute.cluster_resolver.TPUClusterResolver(
                args.tpu_name       ,   zone=args.tpu_zone       ,   project=args.gcp_project   )
        else:
            _UpperCAmelCase        = tf.distribute.cluster_resolver.TPUClusterResolver()
    except ValueError:
        raise RuntimeError(
            """Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or """
            """--gcp_project. When running on a TPU VM, use --tpu_name local."""   )

    tf.config.experimental_connect_to_cluster(_A   )
    tf.tpu.experimental.initialize_tpu_system(_A   )

    return tpu


def 			_UpperCamelCase						(    _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = 0
    for file in file_list:
        _UpperCAmelCase        = file.split("""/"""   )[-1]
        _UpperCAmelCase        = re.search(R"""-\d+-(\d+)\.tfrecord"""       ,   _A   ).group(1   )
        _UpperCAmelCase        = int(_A   )
        num_samples += sample_count

    return num_samples


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A       ,   _A=None   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = count_samples(_A   )
    _UpperCAmelCase        = tf.data.Dataset.from_tensor_slices(_A   )
    if shuffle:
        _UpperCAmelCase        = dataset.shuffle(len(_A   )   )
    _UpperCAmelCase        = tf.data.TFRecordDataset(_A       ,   num_parallel_reads=_A   )
    # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
    _UpperCAmelCase        = dataset.apply(tf.data.experimental.assert_cardinality(_A   )   )
    _UpperCAmelCase        = dataset.map(_A       ,   num_parallel_calls=_A   )
    if shuffle:
        assert shuffle_buffer_size is not None
        _UpperCAmelCase        = dataset.shuffle(args.shuffle_buffer_size   )
    _UpperCAmelCase        = dataset.batch(_A       ,   drop_remainder=_A   )
    _UpperCAmelCase        = dataset.map(_A       ,   num_parallel_calls=_A   )
    _UpperCAmelCase        = dataset.prefetch(_A   )
    return dataset


def 			_UpperCamelCase						(    _A   )     ->	Optional[int]:





    """simple docstring"""

    if not args.no_tpu:
        _UpperCAmelCase        = initialize_tpu(_A   )
        _UpperCAmelCase        = tf.distribute.TPUStrategy(_A   )
    else:
        _UpperCAmelCase        = tf.distribute.OneDeviceStrategy(device="""/gpu:0"""   )

    if args.bfloataa:
        tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""   )

    _UpperCAmelCase        = AutoTokenizer.from_pretrained(args.tokenizer   )
    _UpperCAmelCase        = AutoConfig.from_pretrained(args.pretrained_model_config   )
    _UpperCAmelCase        = tokenizer.vocab_size

    _UpperCAmelCase        = tf.io.gfile.glob(os.path.join(args.train_dataset       ,   """*.tfrecord"""   )   )
    if not training_records:
        raise ValueError(F"""No .tfrecord files found in {args.train_dataset}."""   )
    _UpperCAmelCase        = tf.io.gfile.glob(os.path.join(args.eval_dataset       ,   """*.tfrecord"""   )   )
    if not eval_records:
        raise ValueError(F"""No .tfrecord files found in {args.eval_dataset}."""   )

    _UpperCAmelCase        = count_samples(_A   )

    _UpperCAmelCase        = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
    _UpperCAmelCase        = steps_per_epoch * args.num_epochs

    with strategy.scope():
        _UpperCAmelCase        = TFAutoModelForMaskedLM.from_config(_A   )
        model(model.dummy_inputs   )  # Pass some dummy inputs through the model to ensure all the weights are built
        _UpperCAmelCase     ,_UpperCAmelCase        = create_optimizer(
            num_train_steps=_A       ,   num_warmup_steps=total_train_steps // 2_0       ,   init_lr=args.learning_rate       ,   weight_decay_rate=args.weight_decay_rate       ,   )

        # Transformers models compute the right loss for their task by default when labels are passed, and will
        # use this for training unless you specify your own loss function in compile().
        model.compile(optimizer=_A       ,   metrics=["""accuracy"""]   )

    def decode_fn(_A   ):
        _UpperCAmelCase        = {
            """input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa       ,   shape=(args.max_length,)   ),
            """attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa       ,   shape=(args.max_length,)   ),
        }
        return tf.io.parse_single_example(_A       ,   _A   )

    # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
    # use their methods in our data pipeline.
    _UpperCAmelCase        = DataCollatorForLanguageModeling(
        tokenizer=_A       ,   mlm_probability=args.mlm_probability       ,   mlm=_A       ,   return_tensors="""tf"""   )

    def mask_with_collator(_A   ):
        # TF really needs an isin() function
        _UpperCAmelCase        = (
            ~tf.cast(batch["""attention_mask"""]       ,   tf.bool   )
            | (batch["""input_ids"""] == tokenizer.cls_token_id)
            | (batch["""input_ids"""] == tokenizer.sep_token_id)
        )
        _UpperCAmelCase     ,_UpperCAmelCase        = data_collator.tf_mask_tokens(
            batch["""input_ids"""]       ,   vocab_size=len(_A   )       ,   mask_token_id=tokenizer.mask_token_id       ,   special_tokens_mask=_A       ,   )
        return batch

    _UpperCAmelCase        = args.per_replica_batch_size * strategy.num_replicas_in_sync

    _UpperCAmelCase        = prepare_dataset(
        _A       ,   decode_fn=_A       ,   mask_fn=_A       ,   batch_size=_A       ,   shuffle=_A       ,   shuffle_buffer_size=args.shuffle_buffer_size       ,   )

    _UpperCAmelCase        = prepare_dataset(
        _A       ,   decode_fn=_A       ,   mask_fn=_A       ,   batch_size=_A       ,   shuffle=_A       ,   )

    _UpperCAmelCase        = []
    if args.hub_model_id:
        callbacks.append(
            PushToHubCallback(output_dir=args.output_dir       ,   hub_model_id=args.hub_model_id       ,   tokenizer=_A   )   )

    model.fit(
        _A       ,   validation_data=_A       ,   epochs=args.num_epochs       ,   callbacks=_A       ,   )

    model.save_pretrained(args.output_dir   )


if __name__ == "__main__":
 a       :						int       		=		parse_args()
 main(args)

"""simple docstring"""



import math




class 		a_    :







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							list[list[float]]  ,       __UpperCamelCase      :							list[int]  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = 0.0
        _UpperCAmelCase        = 0.0
        for i in range(len(__UpperCamelCase  )  ):
            da += math.pow((sample[i] - weights[0][i])  ,       2  )
            da += math.pow((sample[i] - weights[1][i])  ,       2  )
            return 0 if da > da else 1
        return 0







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							list[list[int | float]]  ,       __UpperCamelCase      :							list[int]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							float  )		->list[list[int | float]]:






        '''simple docstring'''






        for i in range(len(__UpperCamelCase  )  ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights







def 			_UpperCamelCase						(    )     ->	None:





    """simple docstring"""

    _UpperCAmelCase        = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    _UpperCAmelCase        = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    _UpperCAmelCase        = SelfOrganizingMap()
    _UpperCAmelCase        = 3
    _UpperCAmelCase        = 0.5

    for _ in range(_A   ):
        for j in range(len(_A   )   ):
            # training sample
            _UpperCAmelCase        = training_samples[j]

            # Compute the winning vector
            _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

            # Update the winning vector
            _UpperCAmelCase        = self_organizing_map.update(_A       ,   _A       ,   _A       ,   _A   )

    # classify test sample
    _UpperCAmelCase        = [0, 0, 0, 1]
    _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

    # results
    print(F"""Clusters that the test sample belongs to : {winner}"""   )
    print(F"""Weights that have been trained : {weights}"""   )


# running the main() function
if __name__ == "__main__":
 main()